Overview

Dataset statistics

Number of variables21
Number of observations243787
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.1 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Text11

Alerts

AccountAge is highly overall correlated with TotalChargesHigh correlation
TotalCharges is highly overall correlated with AccountAgeHigh correlation
MonthlyCharges has unique valuesUnique
TotalCharges has unique valuesUnique
ViewingHoursPerWeek has unique valuesUnique
AverageViewingDuration has unique valuesUnique
UserRating has unique valuesUnique
CustomerID has unique valuesUnique
ContentDownloadsPerMonth has 4851 (2.0%) zerosZeros
SupportTicketsPerMonth has 24292 (10.0%) zerosZeros
WatchlistSize has 9654 (4.0%) zerosZeros
Churn has 199605 (81.9%) zerosZeros

Reproduction

Analysis started2023-10-19 00:23:31.542677
Analysis finished2023-10-19 00:24:05.690003
Duration34.15 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

AccountAge
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.083758
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:05.860601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q130
median60
Q390
95-th percentile114
Maximum119
Range118
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.285143
Coefficient of variation (CV)0.57062249
Kurtosis-1.1992817
Mean60.083758
Median Absolute Deviation (MAD)30
Skewness-0.002506029
Sum14647639
Variance1175.471
MonotonicityNot monotonic
2023-10-18T17:24:06.119288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 2168
 
0.9%
95 2157
 
0.9%
19 2148
 
0.9%
74 2143
 
0.9%
99 2141
 
0.9%
87 2131
 
0.9%
60 2131
 
0.9%
92 2130
 
0.9%
13 2114
 
0.9%
76 2114
 
0.9%
Other values (109) 222410
91.2%
ValueCountFrequency (%)
1 2015
0.8%
2 1969
0.8%
3 2013
0.8%
4 2028
0.8%
5 1967
0.8%
6 2088
0.9%
7 2054
0.8%
8 1974
0.8%
9 2001
0.8%
10 1953
0.8%
ValueCountFrequency (%)
119 2071
0.8%
118 2008
0.8%
117 2066
0.8%
116 2064
0.8%
115 2017
0.8%
114 2006
0.8%
113 1990
0.8%
112 2102
0.9%
111 2090
0.9%
110 2069
0.8%

MonthlyCharges
Real number (ℝ)

UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.490695
Minimum4.9900615
Maximum19.989957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:06.376128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.9900615
5-th percentile5.7419177
Q18.7385432
median12.495555
Q316.23816
95-th percentile19.226437
Maximum19.989957
Range14.999895
Interquartile range (IQR)7.499617

Descriptive statistics

Standard deviation4.3276154
Coefficient of variation (CV)0.34646716
Kurtosis-1.2015094
Mean12.490695
Median Absolute Deviation (MAD)3.7495368
Skewness-0.0035843795
Sum3045068.9
Variance18.728255
MonotonicityNot monotonic
2023-10-18T17:24:06.644410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0552151 1
 
< 0.1%
12.88378931 1
 
< 0.1%
14.83776403 1
 
< 0.1%
5.887315074 1
 
< 0.1%
13.36096719 1
 
< 0.1%
17.18843146 1
 
< 0.1%
19.33623845 1
 
< 0.1%
13.53752198 1
 
< 0.1%
19.15785687 1
 
< 0.1%
16.56393002 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
4.990061547 1
< 0.1%
4.990112126 1
< 0.1%
4.990126433 1
< 0.1%
4.990184837 1
< 0.1%
4.990326069 1
< 0.1%
4.990379425 1
< 0.1%
4.990441189 1
< 0.1%
4.99048508 1
< 0.1%
4.990630161 1
< 0.1%
4.99079638 1
< 0.1%
ValueCountFrequency (%)
19.98995687 1
< 0.1%
19.98982121 1
< 0.1%
19.98975519 1
< 0.1%
19.98974143 1
< 0.1%
19.98961734 1
< 0.1%
19.98951769 1
< 0.1%
19.98948893 1
< 0.1%
19.98942455 1
< 0.1%
19.98941153 1
< 0.1%
19.98936381 1
< 0.1%

TotalCharges
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.74102
Minimum4.9911544
Maximum2378.7238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:06.904247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.9911544
5-th percentile71.255304
Q1329.14703
median649.87849
Q31089.3174
95-th percentile1766.0831
Maximum2378.7238
Range2373.7327
Interquartile range (IQR)760.17034

Descriptive statistics

Standard deviation523.07327
Coefficient of variation (CV)0.69674263
Kurtosis-0.26204684
Mean750.74102
Median Absolute Deviation (MAD)364.48602
Skewness0.69406771
Sum1.830209 × 108
Variance273605.65
MonotonicityNot monotonic
2023-10-18T17:24:07.145865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221.104302 1
 
< 0.1%
1159.541038 1
 
< 0.1%
1721.180628 1
 
< 0.1%
359.1262195 1
 
< 0.1%
1042.155441 1
 
< 0.1%
1598.524126 1
 
< 0.1%
1411.545407 1
 
< 0.1%
974.7015825 1
 
< 0.1%
1264.418553 1
 
< 0.1%
265.0228803 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
4.991154354 1
< 0.1%
4.999861067 1
< 0.1%
5.018304003 1
< 0.1%
5.033848266 1
< 0.1%
5.047382883 1
< 0.1%
5.053362973 1
< 0.1%
5.065943606 1
< 0.1%
5.066787708 1
< 0.1%
5.081493533 1
< 0.1%
5.08541407 1
< 0.1%
ValueCountFrequency (%)
2378.723844 1
< 0.1%
2378.454499 1
< 0.1%
2377.774305 1
< 0.1%
2377.224228 1
< 0.1%
2375.495398 1
< 0.1%
2374.015612 1
< 0.1%
2373.837145 1
< 0.1%
2373.70029 1
< 0.1%
2373.557275 1
< 0.1%
2372.488799 1
< 0.1%

SubscriptionType
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:07.354293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.6711063
Min length5

Characters and Unicode

Total characters1626329
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowBasic
3rd rowBasic
4th rowBasic
5th rowPremium
ValueCountFrequency (%)
standard 81920
33.6%
basic 81050
33.2%
premium 80817
33.2%
2023-10-18T17:24:07.790762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1382542
85.0%
Uppercase Letter 243787
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 244890
17.7%
d 163840
11.9%
r 162737
11.8%
i 161867
11.7%
m 161634
11.7%
t 81920
 
5.9%
n 81920
 
5.9%
s 81050
 
5.9%
c 81050
 
5.9%
e 80817
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
S 81920
33.6%
B 81050
33.2%
P 80817
33.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1626329
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1626329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

PaymentMethod
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:08.009521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length13
Mean length13.005488
Min length11

Characters and Unicode

Total characters3170569
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMailed check
2nd rowCredit card
3rd rowMailed check
4th rowElectronic check
5th rowElectronic check
ValueCountFrequency (%)
check 122066
25.0%
electronic 61313
12.6%
credit 60924
12.5%
card 60924
12.5%
bank 60797
12.5%
transfer 60797
12.5%
mailed 60753
12.5%
2023-10-18T17:24:08.442007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2682995
84.6%
Space Separator 243787
 
7.7%
Uppercase Letter 243787
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 427682
15.9%
e 365853
13.6%
r 304755
11.4%
a 243271
9.1%
t 183034
6.8%
i 182990
6.8%
n 182907
6.8%
k 182863
6.8%
d 182601
6.8%
l 122066
 
4.5%
Other values (4) 304973
11.4%
Uppercase Letter
ValueCountFrequency (%)
E 61313
25.2%
C 60924
25.0%
B 60797
24.9%
M 60753
24.9%
Space Separator
ValueCountFrequency (%)
243787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2926782
92.3%
Common 243787
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 427682
14.6%
e 365853
12.5%
r 304755
10.4%
a 243271
8.3%
t 183034
 
6.3%
i 182990
 
6.3%
n 182907
 
6.2%
k 182863
 
6.2%
d 182601
 
6.2%
l 122066
 
4.2%
Other values (8) 548760
18.7%
Common
ValueCountFrequency (%)
243787
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3170569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%

PaperlessBilling
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:08.643356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.4996452
Min length2

Characters and Unicode

Total characters609381
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowNo
5th rowYes
ValueCountFrequency (%)
no 121980
50.0%
yes 121807
50.0%
2023-10-18T17:24:09.044136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 121980
20.0%
o 121980
20.0%
Y 121807
20.0%
e 121807
20.0%
s 121807
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365594
60.0%
Uppercase Letter 243787
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 121980
33.4%
e 121807
33.3%
s 121807
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 121980
50.0%
Y 121807
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 609381
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 121980
20.0%
o 121980
20.0%
Y 121807
20.0%
e 121807
20.0%
s 121807
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 121980
20.0%
o 121980
20.0%
Y 121807
20.0%
e 121807
20.0%
s 121807
20.0%

ContentType
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:09.265211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9951433
Min length4

Characters and Unicode

Total characters1461538
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBoth
2nd rowMovies
3rd rowMovies
4th rowTV Shows
5th rowTV Shows
ValueCountFrequency (%)
both 81737
25.2%
tv 81145
25.0%
shows 81145
25.0%
movies 80905
24.9%
2023-10-18T17:24:09.718092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 974316
66.7%
Uppercase Letter 406077
27.8%
Space Separator 81145
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 243787
25.0%
h 162882
16.7%
s 162050
16.6%
t 81737
 
8.4%
w 81145
 
8.3%
v 80905
 
8.3%
i 80905
 
8.3%
e 80905
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
B 81737
20.1%
T 81145
20.0%
V 81145
20.0%
S 81145
20.0%
M 80905
19.9%
Space Separator
ValueCountFrequency (%)
81145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1380393
94.4%
Common 81145
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 243787
17.7%
h 162882
11.8%
s 162050
11.7%
B 81737
 
5.9%
t 81737
 
5.9%
T 81145
 
5.9%
V 81145
 
5.9%
S 81145
 
5.9%
w 81145
 
5.9%
M 80905
 
5.9%
Other values (3) 242715
17.6%
Common
ValueCountFrequency (%)
81145
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1461538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%

MultiDeviceAccess
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:09.909820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.4994196
Min length2

Characters and Unicode

Total characters609326
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 122035
50.1%
yes 121752
49.9%
2023-10-18T17:24:10.310761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 122035
20.0%
o 122035
20.0%
Y 121752
20.0%
e 121752
20.0%
s 121752
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365539
60.0%
Uppercase Letter 243787
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 122035
33.4%
e 121752
33.3%
s 121752
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 122035
50.1%
Y 121752
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 609326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 122035
20.0%
o 122035
20.0%
Y 121752
20.0%
e 121752
20.0%
s 121752
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 122035
20.0%
o 122035
20.0%
Y 121752
20.0%
e 121752
20.0%
s 121752
20.0%

DeviceRegistered
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:10.581693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5076112
Min length2

Characters and Unicode

Total characters1342684
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowTablet
3rd rowComputer
4th rowTablet
5th rowTV
ValueCountFrequency (%)
computer 61147
25.1%
tablet 61143
25.1%
mobile 60914
25.0%
tv 60583
24.9%
2023-10-18T17:24:11.039727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1038314
77.3%
Uppercase Letter 304370
 
22.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 183204
17.6%
t 122290
11.8%
o 122061
11.8%
b 122057
11.8%
l 122057
11.8%
m 61147
 
5.9%
p 61147
 
5.9%
u 61147
 
5.9%
r 61147
 
5.9%
a 61143
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
T 121726
40.0%
C 61147
20.1%
M 60914
20.0%
V 60583
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1342684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1342684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

ViewingHoursPerWeek
Real number (ℝ)

UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.502179
Minimum1.0000654
Maximum39.999723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:11.511162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.0000654
5-th percentile2.9556786
Q110.763953
median20.523116
Q330.219396
95-th percentile38.02732
Maximum39.999723
Range38.999658
Interquartile range (IQR)19.455443

Descriptive statistics

Standard deviation11.243753
Coefficient of variation (CV)0.54841748
Kurtosis-1.1998167
Mean20.502179
Median Absolute Deviation (MAD)9.7293099
Skewness-0.0013398264
Sum4998164.7
Variance126.42199
MonotonicityNot monotonic
2023-10-18T17:24:11.778876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.75810391 1
 
< 0.1%
17.59053039 1
 
< 0.1%
35.68335488 1
 
< 0.1%
5.693638995 1
 
< 0.1%
19.23610809 1
 
< 0.1%
4.379284843 1
 
< 0.1%
31.6819025 1
 
< 0.1%
10.10222013 1
 
< 0.1%
14.87022052 1
 
< 0.1%
32.57026882 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
1.000065389 1
< 0.1%
1.000086097 1
< 0.1%
1.000133293 1
< 0.1%
1.00018081 1
< 0.1%
1.000248228 1
< 0.1%
1.000325918 1
< 0.1%
1.000397443 1
< 0.1%
1.000443967 1
< 0.1%
1.000756308 1
< 0.1%
1.000915633 1
< 0.1%
ValueCountFrequency (%)
39.99972314 1
< 0.1%
39.99971796 1
< 0.1%
39.99964292 1
< 0.1%
39.99961711 1
< 0.1%
39.99957754 1
< 0.1%
39.99916777 1
< 0.1%
39.9989975 1
< 0.1%
39.99883125 1
< 0.1%
39.9985596 1
< 0.1%
39.99834422 1
< 0.1%

AverageViewingDuration
Real number (ℝ)

UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.264061
Minimum5.0005475
Maximum179.99928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:12.039247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5.0005475
5-th percentile13.69635
Q148.382395
median92.249992
Q3135.90805
95-th percentile171.15799
Maximum179.99928
Range174.99873
Interquartile range (IQR)87.525653

Descriptive statistics

Standard deviation50.505243
Coefficient of variation (CV)0.54739887
Kurtosis-1.2008998
Mean92.264061
Median Absolute Deviation (MAD)43.760873
Skewness0.0027580626
Sum22492779
Variance2550.7796
MonotonicityNot monotonic
2023-10-18T17:24:12.330715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.53137733 1
 
< 0.1%
114.5204177 1
 
< 0.1%
98.66201871 1
 
< 0.1%
60.20356905 1
 
< 0.1%
123.5751547 1
 
< 0.1%
55.49882022 1
 
< 0.1%
116.293516 1
 
< 0.1%
87.49099203 1
 
< 0.1%
153.527901 1
 
< 0.1%
89.43429449 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
5.000547486 1
< 0.1%
5.000937119 1
< 0.1%
5.002643095 1
< 0.1%
5.002748913 1
< 0.1%
5.00341399 1
< 0.1%
5.003741659 1
< 0.1%
5.004575147 1
< 0.1%
5.004932694 1
< 0.1%
5.005317525 1
< 0.1%
5.005780925 1
< 0.1%
ValueCountFrequency (%)
179.9992751 1
< 0.1%
179.9990502 1
< 0.1%
179.9990248 1
< 0.1%
179.998513 1
< 0.1%
179.9984585 1
< 0.1%
179.9978052 1
< 0.1%
179.9976453 1
< 0.1%
179.9968864 1
< 0.1%
179.9966445 1
< 0.1%
179.9964961 1
< 0.1%

ContentDownloadsPerMonth
Real number (ℝ)

ZEROS 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.503513
Minimum0
Maximum49
Zeros4851
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:12.644991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median24
Q337
95-th percentile47
Maximum49
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.421174
Coefficient of variation (CV)0.58853494
Kurtosis-1.2013526
Mean24.503513
Median Absolute Deviation (MAD)13
Skewness-0.00042730885
Sum5973638
Variance207.97025
MonotonicityNot monotonic
2023-10-18T17:24:12.902257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 4999
 
2.1%
8 4996
 
2.0%
39 4995
 
2.0%
23 4959
 
2.0%
40 4952
 
2.0%
43 4952
 
2.0%
44 4951
 
2.0%
4 4949
 
2.0%
33 4929
 
2.0%
42 4922
 
2.0%
Other values (40) 194183
79.7%
ValueCountFrequency (%)
0 4851
2.0%
1 4763
2.0%
2 4817
2.0%
3 4914
2.0%
4 4949
2.0%
5 4897
2.0%
6 4875
2.0%
7 4860
2.0%
8 4996
2.0%
9 4814
2.0%
ValueCountFrequency (%)
49 4877
2.0%
48 4838
2.0%
47 4776
2.0%
46 4867
2.0%
45 4795
2.0%
44 4951
2.0%
43 4952
2.0%
42 4922
2.0%
41 4812
2.0%
40 4952
2.0%

GenrePreference
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:13.118098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0008655
Min length5

Characters and Unicode

Total characters1462933
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSci-Fi
2nd rowAction
3rd rowFantasy
4th rowDrama
5th rowComedy
ValueCountFrequency (%)
comedy 49060
20.1%
fantasy 48955
20.1%
drama 48744
20.0%
action 48690
20.0%
sci-fi 48338
19.8%
2023-10-18T17:24:13.610367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1122470
76.7%
Uppercase Letter 292125
 
20.0%
Dash Punctuation 48338
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 195398
17.4%
i 145366
13.0%
y 98015
8.7%
m 97804
8.7%
o 97750
8.7%
t 97645
8.7%
n 97645
8.7%
c 97028
8.6%
d 49060
 
4.4%
e 49060
 
4.4%
Other values (2) 97699
8.7%
Uppercase Letter
ValueCountFrequency (%)
F 97293
33.3%
C 49060
16.8%
D 48744
16.7%
A 48690
16.7%
S 48338
16.5%
Dash Punctuation
ValueCountFrequency (%)
- 48338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1414595
96.7%
Common 48338
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 195398
13.8%
i 145366
10.3%
y 98015
 
6.9%
m 97804
 
6.9%
o 97750
 
6.9%
t 97645
 
6.9%
n 97645
 
6.9%
F 97293
 
6.9%
c 97028
 
6.9%
C 49060
 
3.5%
Other values (7) 341591
24.1%
Common
ValueCountFrequency (%)
- 48338
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1462933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

UserRating
Real number (ℝ)

UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0027127
Minimum1.0000074
Maximum4.9999894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:13.880617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.0000074
5-th percentile1.2026707
Q12.000853
median3.0022614
Q34.0021573
95-th percentile4.8019608
Maximum4.9999894
Range3.999982
Interquartile range (IQR)2.0013043

Descriptive statistics

Standard deviation1.1552591
Coefficient of variation (CV)0.38473848
Kurtosis-1.2018115
Mean3.0027127
Median Absolute Deviation (MAD)1.0006059
Skewness-0.00095780411
Sum732022.33
Variance1.3346237
MonotonicityNot monotonic
2023-10-18T17:24:14.162453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.176497515 1
 
< 0.1%
1.695150915 1
 
< 0.1%
2.295504648 1
 
< 0.1%
2.696378122 1
 
< 0.1%
4.882394783 1
 
< 0.1%
1.745561648 1
 
< 0.1%
2.723127467 1
 
< 0.1%
2.313576574 1
 
< 0.1%
3.045026176 1
 
< 0.1%
3.71845463 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
1.000007378 1
< 0.1%
1.000039325 1
< 0.1%
1.000049907 1
< 0.1%
1.000052364 1
< 0.1%
1.000057666 1
< 0.1%
1.000068362 1
< 0.1%
1.000080538 1
< 0.1%
1.000082498 1
< 0.1%
1.000104797 1
< 0.1%
1.000130054 1
< 0.1%
ValueCountFrequency (%)
4.999989412 1
< 0.1%
4.999982428 1
< 0.1%
4.999973277 1
< 0.1%
4.99996787 1
< 0.1%
4.999942379 1
< 0.1%
4.999936482 1
< 0.1%
4.999934065 1
< 0.1%
4.999909322 1
< 0.1%
4.999849698 1
< 0.1%
4.999844547 1
< 0.1%

SupportTicketsPerMonth
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.504186
Minimum0
Maximum9
Zeros24292
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:14.385198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8725484
Coefficient of variation (CV)0.63775082
Kurtosis-1.2255379
Mean4.504186
Median Absolute Deviation (MAD)3
Skewness-0.00089641856
Sum1098062
Variance8.251534
MonotonicityNot monotonic
2023-10-18T17:24:14.570963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 24626
10.1%
4 24618
10.1%
2 24477
10.0%
9 24435
10.0%
8 24400
10.0%
3 24360
10.0%
6 24296
10.0%
0 24292
10.0%
1 24283
10.0%
5 24000
9.8%
ValueCountFrequency (%)
0 24292
10.0%
1 24283
10.0%
2 24477
10.0%
3 24360
10.0%
4 24618
10.1%
5 24000
9.8%
6 24296
10.0%
7 24626
10.1%
8 24400
10.0%
9 24435
10.0%
ValueCountFrequency (%)
9 24435
10.0%
8 24400
10.0%
7 24626
10.1%
6 24296
10.0%
5 24000
9.8%
4 24618
10.1%
3 24360
10.0%
2 24477
10.0%
1 24283
10.0%
0 24292
10.0%

Gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:14.727798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.0002994
Min length4

Characters and Unicode

Total characters1219008
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale
ValueCountFrequency (%)
female 121930
50.0%
male 121857
50.0%
2023-10-18T17:24:15.161419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 975221
80.0%
Uppercase Letter 243787
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 365717
37.5%
a 243787
25.0%
l 243787
25.0%
m 121930
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
F 121930
50.0%
M 121857
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1219008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1219008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

WatchlistSize
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.018508
Minimum0
Maximum24
Zeros9654
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:15.393950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile23
Maximum24
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1930342
Coefficient of variation (CV)0.59849644
Kurtosis-1.1995119
Mean12.018508
Median Absolute Deviation (MAD)6
Skewness-0.0045001718
Sum2929956
Variance51.739741
MonotonicityNot monotonic
2023-10-18T17:24:15.616932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16 9945
 
4.1%
14 9882
 
4.1%
10 9862
 
4.0%
17 9860
 
4.0%
19 9839
 
4.0%
7 9825
 
4.0%
12 9820
 
4.0%
21 9819
 
4.0%
18 9799
 
4.0%
11 9793
 
4.0%
Other values (15) 145343
59.6%
ValueCountFrequency (%)
0 9654
4.0%
1 9600
3.9%
2 9691
4.0%
3 9652
4.0%
4 9698
4.0%
5 9705
4.0%
6 9769
4.0%
7 9825
4.0%
8 9745
4.0%
9 9739
4.0%
ValueCountFrequency (%)
24 9618
3.9%
23 9684
4.0%
22 9737
4.0%
21 9819
4.0%
20 9707
4.0%
19 9839
4.0%
18 9799
4.0%
17 9860
4.0%
16 9945
4.1%
15 9759
4.0%

ParentalControl
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:15.773062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.5007855
Min length2

Characters and Unicode

Total characters609659
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo
ValueCountFrequency (%)
yes 122085
50.1%
no 121702
49.9%
2023-10-18T17:24:16.175127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 122085
20.0%
e 122085
20.0%
s 122085
20.0%
N 121702
20.0%
o 121702
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365872
60.0%
Uppercase Letter 243787
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 122085
33.4%
s 122085
33.4%
o 121702
33.3%
Uppercase Letter
ValueCountFrequency (%)
Y 122085
50.1%
N 121702
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 609659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 122085
20.0%
e 122085
20.0%
s 122085
20.0%
N 121702
20.0%
o 121702
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 122085
20.0%
e 122085
20.0%
s 122085
20.0%
N 121702
20.0%
o 121702
20.0%

SubtitlesEnabled
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:16.356028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.5011752
Min length2

Characters and Unicode

Total characters609754
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowNo
ValueCountFrequency (%)
yes 122180
50.1%
no 121607
49.9%
2023-10-18T17:24:16.759469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 122180
20.0%
e 122180
20.0%
s 122180
20.0%
N 121607
19.9%
o 121607
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365967
60.0%
Uppercase Letter 243787
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 122180
33.4%
s 122180
33.4%
o 121607
33.2%
Uppercase Letter
ValueCountFrequency (%)
Y 122180
50.1%
N 121607
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 609754
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 122180
20.0%
e 122180
20.0%
s 122180
20.0%
N 121607
19.9%
o 121607
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 122180
20.0%
e 122180
20.0%
s 122180
20.0%
N 121607
19.9%
o 121607
19.9%

CustomerID
Text

UNIQUE 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:17.347733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2437870
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243787 ?
Unique (%)100.0%

Sample

1st rowCB6SXPNVZA
2nd rowS7R2G87O09
3rd rowEASDC20BDT
4th rowNPF69NT69N
5th row4LGYPK7VOL
ValueCountFrequency (%)
cb6sxpnvza 1
 
< 0.1%
qfp5alfkj5 1
 
< 0.1%
a8421ll8kc 1
 
< 0.1%
iqnesr4w65 1
 
< 0.1%
easdc20bdt 1
 
< 0.1%
npf69nt69n 1
 
< 0.1%
4lgypk7vol 1
 
< 0.1%
jy5hs0gwhw 1
 
< 0.1%
79xso6p5o3 1
 
< 0.1%
2ldc9aq3c5 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
2023-10-18T17:24:18.144130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1760502
72.2%
Decimal Number 677368
 
27.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 68214
 
3.9%
X 68013
 
3.9%
G 67974
 
3.9%
O 67962
 
3.9%
N 67961
 
3.9%
Z 67949
 
3.9%
R 67948
 
3.9%
D 67937
 
3.9%
K 67884
 
3.9%
T 67850
 
3.9%
Other values (16) 1080810
61.4%
Decimal Number
ValueCountFrequency (%)
6 68024
10.0%
1 67936
10.0%
4 67881
10.0%
2 67864
10.0%
7 67797
10.0%
8 67794
10.0%
5 67733
10.0%
9 67666
10.0%
0 67637
10.0%
3 67036
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1760502
72.2%
Common 677368
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 68214
 
3.9%
X 68013
 
3.9%
G 67974
 
3.9%
O 67962
 
3.9%
N 67961
 
3.9%
Z 67949
 
3.9%
R 67948
 
3.9%
D 67937
 
3.9%
K 67884
 
3.9%
T 67850
 
3.9%
Other values (16) 1080810
61.4%
Common
ValueCountFrequency (%)
6 68024
10.0%
1 67936
10.0%
4 67881
10.0%
2 67864
10.0%
7 67797
10.0%
8 67794
10.0%
5 67733
10.0%
9 67666
10.0%
0 67637
10.0%
3 67036
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2437870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Churn
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18123198
Minimum0
Maximum1
Zeros199605
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-10-18T17:24:18.389675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38521105
Coefficient of variation (CV)2.1255137
Kurtosis0.73917698
Mean0.18123198
Median Absolute Deviation (MAD)0
Skewness1.6550441
Sum44182
Variance0.14838756
MonotonicityNot monotonic
2023-10-18T17:24:18.574919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%
ValueCountFrequency (%)
1 44182
 
18.1%
0 199605
81.9%

Interactions

2023-10-18T17:24:00.525916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:40.560373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:42.868937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:45.067427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:47.154911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:49.374811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:51.578412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:53.721876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:56.079874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:58.160605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:00.725659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:40.813897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:43.083146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:45.289535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:47.389423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:49.590916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:51.794986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:53.945515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:56.281607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:58.367875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:00.945775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:41.034030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:43.317598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:45.497592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:47.616648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:49.815567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:52.011911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:54.263811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:56.501360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:58.826137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:01.133096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:41.232973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:43.526867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:45.691020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:47.817736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:50.022872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:52.210133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:54.496567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:56.688726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:59.021497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:01.357185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:41.462370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:43.757848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:45.910541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:48.045644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:50.251439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:52.439788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:54.735794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:56.907384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:59.237598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:01.585309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:41.690150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:43.983845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:46.121696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:48.272848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:50.490233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:52.655276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:54.967846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:57.125685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:59.462409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:01.780510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:41.900245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:44.196524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:46.333204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:48.496515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:50.713940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:52.851861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:55.181421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:57.329511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:59.664257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:02.003393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:42.169375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:44.433815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:46.556702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:48.727932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:50.949250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:53.089105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:55.413806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:57.567259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:59.891467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:02.195334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:42.437673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:44.637371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:46.756603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:48.942685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:51.151609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:53.297240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:55.624193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:57.760148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:00.113098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:02.392665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:42.657945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:44.862193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:46.958660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:49.152090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:51.362887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:53.507043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:55.846874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:23:57.960568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-18T17:24:00.308906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-18T17:24:18.722227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AccountAgeMonthlyChargesTotalChargesViewingHoursPerWeekAverageViewingDurationContentDownloadsPerMonthUserRatingSupportTicketsPerMonthWatchlistSizeChurn
AccountAge1.0000.0020.856-0.0020.0000.0010.000-0.002-0.003-0.198
MonthlyCharges0.0021.0000.459-0.003-0.001-0.0000.0000.000-0.0010.100
TotalCharges0.8560.4591.000-0.003-0.0010.0000.000-0.002-0.002-0.131
ViewingHoursPerWeek-0.002-0.003-0.0031.0000.0010.002-0.0030.001-0.001-0.129
AverageViewingDuration0.000-0.001-0.0010.0011.000-0.002-0.000-0.0000.001-0.147
ContentDownloadsPerMonth0.001-0.0000.0000.002-0.0021.0000.001-0.0000.002-0.130
UserRating0.0000.0000.000-0.003-0.0000.0011.000-0.0000.0030.022
SupportTicketsPerMonth-0.0020.000-0.0020.001-0.000-0.000-0.0001.0000.0010.084
WatchlistSize-0.003-0.001-0.002-0.0010.0010.0020.0030.0011.0000.022
Churn-0.1980.100-0.131-0.129-0.147-0.1300.0220.0840.0221.000

Missing values

2023-10-18T17:24:02.778355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-18T17:24:03.903661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AccountAgeMonthlyChargesTotalChargesSubscriptionTypePaymentMethodPaperlessBillingContentTypeMultiDeviceAccessDeviceRegisteredViewingHoursPerWeekAverageViewingDurationContentDownloadsPerMonthGenrePreferenceUserRatingSupportTicketsPerMonthGenderWatchlistSizeParentalControlSubtitlesEnabledCustomerIDChurn
02011.055215221.104302PremiumMailed checkNoBothNoMobile36.75810463.53137710Sci-Fi2.1764984Male3NoNoCB6SXPNVZA0
1575.175208294.986882BasicCredit cardYesMoviesNoTablet32.45056825.72559518Action3.4786328Male23NoYesS7R2G87O090
27312.106657883.785952BasicMailed checkYesMoviesNoComputer7.39516057.36406123Fantasy4.2388246Male1YesYesEASDC20BDT0
3327.263743232.439774BasicElectronic checkNoTV ShowsNoTablet27.960389131.53750730Drama4.2760132Male24YesYesNPF69NT69N0
45716.953078966.325422PremiumElectronic checkYesTV ShowsNoTV20.08339745.35665320Comedy3.6161704Female0NoNo4LGYPK7VOL0
51137.295744824.419081PremiumMailed checkYesBothNoMobile21.67829097.09574635Comedy3.7211348Female2YesYesJY5HS0GWHW0
63812.340675468.945639PremiumBank transferNoBothNoComputer36.51276181.78299328Action4.0908689Female20NoYes79XSO6P5O30
7257.247550181.188753StandardElectronic checkYesTV ShowsNoTV16.355816154.52168210Fantasy3.4102212Female22NoNo2LDC9AQ3C50
82619.803233514.884050StandardBank transferNoMoviesNoTablet8.20292994.37521128Fantasy2.6799860Male5YesYes74DURHL3Y81
91418.842934263.801080StandardBank transferNoMoviesNoComputer38.560694122.0128900Comedy2.9934410Male18NoNoCY8S2R3A1T0
AccountAgeMonthlyChargesTotalChargesSubscriptionTypePaymentMethodPaperlessBillingContentTypeMultiDeviceAccessDeviceRegisteredViewingHoursPerWeekAverageViewingDurationContentDownloadsPerMonthGenrePreferenceUserRatingSupportTicketsPerMonthGenderWatchlistSizeParentalControlSubtitlesEnabledCustomerIDChurn
243777456.582492296.212157StandardCredit cardYesTV ShowsNoTV23.08793137.83232927Drama3.7480991Female12YesYesFQ2HIE4Z9G1
2437784611.598542533.532929BasicMailed checkYesTV ShowsNoComputer32.676961160.03174929Sci-Fi1.8013273Female3YesYesJNHOX08RU40
2437799415.2763031435.972490StandardElectronic checkYesMoviesNoComputer28.67772086.02592019Sci-Fi1.0501238Male24YesNoAWR6P119AJ0
2437801810.444138187.994487StandardMailed checkYesMoviesYesTV8.91434624.65808123Fantasy1.4179457Male3YesYesPQQRAZXQ5U0
2437813513.499269472.474410StandardElectronic checkNoTV ShowsYesComputer34.26211133.14782017Comedy3.5656723Male6NoNoPBWH0TU5H70
243782779.639902742.272460BasicMailed checkNoMoviesNoComputer13.50272980.36731247Sci-Fi3.6974511Male8YesNoFBZ38J108Z0
24378311713.0492571526.763053PremiumCredit cardNoTV ShowsYesTV24.96329159.81844135Comedy1.4497424Male20NoNoW4AO1Y6NAI0
24378411314.5145691640.146267PremiumCredit cardYesTV ShowsNoTV10.628728176.18609544Action4.0122176Male13YesYes0H3SWWI7IU0
243785718.140555126.983887PremiumBank transferYesTV ShowsNoTV30.466782153.38631536Fantasy2.1357897Female5NoYes63SJ44RT4A0
2437869011.5937741043.439704PremiumMailed checkNoBothNoTV24.97253784.82449811Action1.4288963Female1YesNoA6IN701VRY0